Austin Community College · Higher Education · Austin, TX · 14 weeks
Student success analytics deployed to 220 advisors
Austin Community College relied on a manual process to identify at-risk students, which took three weeks each semester. This delay prevented timely intervention. We developed a student success analytics platform that flagged at-risk students within 48 hours of early warning signs, giving advisors enough time to act before students dropped out.
Challenge
The advising team spent three weeks each semester manually gathering enrollment, grade, and attendance data from three different systems and compiling it in Excel. This delayed identifying at-risk students, leaving just two to three weeks for interventions before the withdrawal deadline. As a result, the college lost between 1,800 and 2,200 students each semester to avoidable withdrawals.
Approach
We created a Power BI platform that pulls daily data from three systems to calculate an at-risk score for each student using 18 indicators. The platform highlights the highest-risk students in a dashboard for advisors to prioritize their caseloads. We trained 220 advisors on how to use it over three weeks.
Outcome
We cut the time to identify at-risk students from three weeks to two days. In the first semester using the new system, advisors reached out to 1,240 students who were likely to drop out based on past data. This led to a 4.2-point increase in retention, securing $3.1 million in tuition that otherwise would have been lost.
18 Warning Signs We Caught Before Things Went Sideways
Here’s what we did: we built an at-risk score based on 18 different factors that show how students hang in there at community college. Things like missed assignments, grade trends, attendance, whether they got financial aid, shifts in their course load, and how they performed the previous semester. When we combined all these pieces, it gave us a way clearer picture of who was likely to drop out.
We spent our advisor time where it actually made a difference.
We built the platform to flag students who were high-risk. Then, we paired those students with advisors who knew just what to do. That way, advisors spent their time where it counted most, helping students who needed urgent support.
How We Kept 1,240 Students Engaged All Semester Long
We jumped in and helped the college bump up first-semester retention by 4.2 points. That might sound small, but it meant 1,240 more students didn’t drop out. With tuition at $2,500 a semester, that’s an extra $3.1 million in the bank. Frankly, the platform basically paid for itself in just one semester.
Results
- 1,240 Students retained in first semester
- 3 wks to 48 hrs At-risk identification time
- $3.1M Retained tuition revenue
- 220 Advisors using the platform
Before, we only identified at-risk students about three weeks into the semester, so we didn’t have much time to intervene. Now, we catch them within 48 hours. As a result, we prevented 1,240 students from dropping out in the first semester. Those are 1,240 students who stayed enrolled.